Show simple item record

dc.contributor.advisorLiu, Juxinen_US
dc.creatorObeidat, Mohammeden_US
dc.date.accessioned2014-07-18T12:00:15Z
dc.date.available2014-07-18T12:00:15Z
dc.date.created2014-07en_US
dc.date.issued2014-07-17en_US
dc.date.submittedJuly 2014en_US
dc.identifier.urihttp://hdl.handle.net/10388/ETD-2014-07-1589en_US
dc.description.abstractTime series involving count data are present in a wide variety of applications. In many applications, the observed counts are usually small and dependent. Failure to take these facts into account can lead to misleading inferences and may detect false relationships. To tackle such issues, a Poisson parameter-driven model is assumed for the time series at hand. This model can account for the time dependence between observations through introducing an autoregressive latent process. In this thesis, we consider Bayesian approaches for estimating the Poisson parameter-driven model. The main challenge is that the likelihood function for the observed counts involves a high dimensional integral after integrating out the latent variables. The main contributions of this thesis are threefold. First, I develop a new single-move (SM) Markov chain Monte Carlo (MCMC) method to sample the latent variables one by one. Second, I adopt the idea of the particle Gibbs sampler (PGS) method \citep{andrieu} into our model setting and compare its performance with the SM method. Third, I consider Bayesian composite likelihood methods and compare three different adjustment methods with the unadjusted method and the SM method. The comparisons provide a practical guide to what method to use. We conduct simulation studies to compare the latter two methods with the SM method. We conclude that the SM method outperforms the PGS method for small sample size, while they perform almost the same for large sample size. However, the SM method is much faster than the PGS method. The adjusted Bayesian composite methods provide closer results to the SM than the unadjusted one. The PGS and the selected adjustment method from simulation studies are compared with the SM method via a real data example. Similar results are obtained: first, the PGS method provides results very close to those of the SM method. Second, the adjusted composite likelihood methods provide closer results to the SM than the unadjusted one.en_US
dc.language.isoengen_US
dc.subjectTime seriesen_US
dc.subjectcount dataen_US
dc.subjectPoisson parameter-driven modelen_US
dc.subjectcomposite likelihooden_US
dc.subjectparticle Gibbs sampleren_US
dc.subjectcar crashesen_US
dc.titleBayesian analysis for time series of count dataen_US
thesis.degree.departmentMathematics and Statisticsen_US
thesis.degree.disciplineMathematicsen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (Ph.D.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberSoteros, Chrisineen_US
dc.contributor.committeeMemberBickis, Miken_US
dc.contributor.committeeMemberLi, Longhaien_US
dc.contributor.committeeMemberOsgood, Nathanielen_US
dc.contributor.committeeMemberGao, Xinen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record